Dataset Information

ProTargetMiner: A proteome library of anticancer molecules for functional discovery

ABSTRACT: As a further development of our Functional Identification of drug Targets by Expression Proteomics (FITExP) method, a reference dataset was built of the proteomic responses of A549 human adenocarcinoma cells to 56 clinical and experimental anticancer molecules. For each molecule, a concentration was used killing 50% of the cells after 48 h. With 4557 proteins quantified across all treatments and controls, 11 orthogonal dimensions were found in factor analysis of the ProTargetMiner dataset, indicating the action of at least that many distinct cell death modalities. For convenience, the number of dimensions was reduced to three and two, thus producing a “death globe” and “death map”, respectively. As expected, molecules with similar mechanisms of action produced proteome signatures clustering together on the death globe and map. Interrogation of the dataset with partial least square modeling contrasting a given molecule against all other treatments unveiled drug targets and mechanistic proteins, even when the specific protein regulation was as subtle as a 15% change. The mechanisms of action were revealed by mapping the group of target candidates on known protein networks. The dataset allows for unique interrogation approaches, such as defining a set of co-regulated proteins. The core (“untouchable”) proteome consisting of proteins with steady expression levels unaffected by the treatments was also defined; they are found to be involved in proteasome and mRNA surveillance pathway. Finally, a list of proteins most affected by the drugs was compiled; these proteins encompass many known drug targets. The ProTargetMiner represents a resource that may be useful to the community of drug developers or scientists interested in drug action mechanisms and cell death modalities. ProTargetMiner can be easily extended to other drugs.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Lung

DISEASE(S): Lung Cancer

SUBMITTER: Amir Ata Saei

LAB HEAD: Roman Zubarev

PROVIDER: PXD009775 | Pride | 2019-11-01

REPOSITORIES: Pride

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ProTargetMiner as a proteome signature library of anticancer molecules for functional discovery.

Saei Amir Ata AA Beusch Christian Michel CM Chernobrovkin Alexey A Sabatier Pierre P Zhang Bo B Tokat Ülkü Güler ÜG Stergiou Eleni E Gaetani Massimiliano M Végvári Ákos Á Zubarev Roman A RA

Nature communications 20191216 1

Deconvolution of targets and action mechanisms of anticancer compounds is fundamental in drug development. Here, we report on ProTargetMiner as a publicly available expandable proteome signature library of anticancer molecules in cancer cell lines. Based on 287 A549 adenocarcinoma proteomes affected by 56 compounds, the main dataset contains 7,328 proteins and 1,307,859 refined protein-drug pairs. These proteomic signatures cluster by compound targets and action mechanisms. The targets and mecha ...[more]

PMID: 31844049

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Project description:Identifying the Mechanism of Action (MoA) of drugs is critical for the development of new drugs, understanding their side effects, and drug repositioning. However, identifying drug MoA has been challenging and has been traditionally attempted only though large experimental setups with little success. While advances in computational power offers the opportunity to achieve this in-silico, methods to exploit existing computational resources are still in their infancy. To overcome this, we developed a novel method to identify Drug Mechanism of Action using Network Dysregulation (DeMAND). The method is based on the realization that drugs affect the protein activity of their targets, but not necessarily their mRNA expression levels. In contrast, the change in protein activity directly affects the mRNA expression levels of downstream genes. Based on this hypothesis, DeMAND identifies drug MoA by comparing gene expression profiles following drug perturbation with control samples, and computing the change in the individual interactions within a pre-determined integrated transcriptional and post-translational regulatory model (interactome). This dataset includes GEPs in 3 different B-cell lymphoma cell lines (OCI-LY3, OCI-LY7 and U2932) at 6, 12, and 24hrs. 92 FDA approved compounds were used at a concentration of IC20 at 24h. DMSO was used as control at each time-point. A total of 828 samples and 29 control samples were available for analysis. Total RNA was isolated with the RNAqueous-96 Automated Kit (Ambion) on the Janus automated liquid handling system (Perkin Elmer Inc.), quantified by NanoDrop 6000 spectrophotometer and quality checked by Agilent Bioanalyzer. 300ng of each of the samples with RIN value >7 were converted to biotinylated cRNA with the Illumina TotalPrep-96 RNA Amplification Kit (Ambion) using a standard T7-based amplification protocol and hybridized on the Human Genome U219 96-Array Plate (Affymetrix). Hybridization, washing, staining and scanning of the array plates were performed on the GeneTitan Instrument (Affymetrix) according to manufacturer’s protocols.

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